The present invention relates to a system for predicting based on time-series information and a method for retrieving the time-series information, and more particularly to method and system for retrieving time-series information which reflect retrieved time-series information as non-numerical information to a predicted result when the result predicted based on a certain low does not hit, for use in subsequent prediction.
As a system for predicting a future value based on time-series information, dealing business in financial business has been known. The dealing business has a target to predict future prices of bonds, stocks and commodities and make profit by trading them in accordance with the prediction.
As a price prediction method, technical analysis of a chart has been used. In this method, past trading data in a market is modified to display various graphs called charts, and a change of price is predicted by recognizing shapes of the graphs and transition patterns. A systemized example of this method is disclosed in "Proposal of Pattern Recognition Type Reduction System of Plural-Time-Series Data (Development of Decision Making Support System Having Knowledge Base (1))" by Maruoka, Yasunobu and Kida, Information Processing Society of Japan, 41th (1990, the latter term) National Conference, Lecture Papers 2-35, 36. In the disclosed method, each pattern to be recognized is represented by an IF-THEN type rule, and a plurality of condition clauses representing status of time-series data which specifies the pattern are described in the IF part, and an action to be taken when the conditions of the IF part are met is described in the THEN part. Fuzzy theory is applied to the conditions of the IF part and a form of a membership function of a fuzzy aggregate is defined for each condition clause. Predicted changes in the time-series data and output messages are described in the THEN part as the action to be taken when the pattern is met.
"Time-Series Data Prediction By A Neural Network (Prediction of Timing of Stock Trading)" by Ohmoto, Ida and Takeoka, Information Processing Society of Japan, 41th (1990, the latter term) National Conference, Lecture Papers 2-133, 134 discloses a method for predicting a trade timing by structuring a plurality of moduled neural networks having knowledge of relationships between various technical indices and economic indices and a trade timing of TOPIX (which is a weighted average of stock values of the first group listed stocks by the number of stocks issued and which is an index reflecting an average change of the stocks). A trade timing which is an index weighted average of period return is used as teacher data.
In the above prior art methods, the time-series data is predicted based on the numerical data, but since the trading price is not determined merely based on the past numerical data, a success rate was not high in either of those methods. In order to improve the success rate, political and economical affairs which happen from time to time should be taken into consideration because they significantly affect to the price. However, since information on those affairs is represented by a text or a character sequence, it is difficult to numerize it and it cannot be built in the prediction system.
Thus, the prior art methods do not take the political or economical information into the system because of difficulty in numerization although it is important information in the prediction.